Deep generative latent variable models are a popular choice of statistical models that are capable of learning complex data distributions, while simultaneously being an expressive unsupervised learning method.
The ability of such models to efficiently learn the underlying data distribution in the presence of continuous latent variables was shown in the auto-encoding variational Bayes (AEVB) [1]} technique.
Building upon the idea of AEVB, the variational autoencoder (VAE) [1]}, [3]} model learns a low-dimensional latent distribution of the original dataset using a pair of deep neural networks.
